AI Specialist- Native Arabic Speakers

Confidential Company; Abu Dhabi; On-site; AED 320,000 - 520,000; Full time

Job summary

Confidential Company is seeking a senior AI Engineer in the UAE to architect and deliver production-ready LLM applications, including conversational agents and RAG pipelines. You will build multi-agent systems, apply prompt engineering, and integrate with enterprise platforms via REST APIs and event-driven architectures.

You will design agentic workflows, manage vector stores, maintain knowledge bases, and mentor junior engineers while leading client workshops and evolving internal standards.

Qualifications

• 5+ years of professional experience in AI Engineering, Machine Learning, or Applied NLP, with at least 2 years focused on LLMs and Generative AI
• Strong proficiency in Python and experience with Hugging Face Transformer Library, Lang Chain, Lang Graph, FastAPI, and PyTorch
• Deep practical knowledge of LLM fine-tuning, prompt engineering, RAG pipeline design, and agentic AI development
• Hands-on experience with vector databases such as Pinecone, Weaviate, Qdrant, FAISS, or PG Vector, along with embedding models
• Proven ability to deploy and productionize AI solutions in AWS, Azure, or GCP using Docker and Kubernetes
• Experience integrating AI solutions with enterprise platforms through REST APIs, webhooks, or event-driven architectures
• Strong understanding of responsible AI principles, including hallucination mitigation, output evaluation, content safety, and model governance
• Excellent communication skills with the ability to explain complex technical concepts to both technical and business stakeholders
• Demonstrated ability to independently lead AI projects from solution design through production deployment
• Preferred Certifications: AWS Certified Machine Learning – Specialty, Azure AI Engineer, GCP Professional Machine Learning Engineer, or equivalent cloud AI/ML certification; Deep Learning Specialization; LLM Engineering or Generative AI certifications from recognized platforms; Certified Kubernetes Application Developer (CKAD) is an added advantage

Responsibilities

• Architect and deliver production-ready LLM applications including conversational agents, intelligent assistants, and RAG pipelines with advanced retrieval and re-ranking
• Build multi-agent systems using Lang Chain, Lang Graph, Auto Gen, or Crew AI
• Apply prompt engineering techniques to optimize model performance
• Integrate LLM solutions with enterprise systems via REST APIs and event-driven architectures
• Design agentic workflows with tool use, memory management, planning loops, and human-in-the-loop controls
• Build AI-driven automation pipelines across structured and unstructured enterprise data sources
• Design and manage vector stores Pinecone, Weaviate, Qdrant, FAISS, or PG Vector with semantic and hybrid search strategies
• Maintain knowledge bases powering enterprise AI applications, ensuring accuracy and freshness of indexed content
• Mentoring: guide and review the work of associate-level engineers; contribute to reusable frameworks and internal engineering standards
• Lead client workshops, technical discovery sessions, and proof-of-concept demonstrations; produce clear solution design documentation
• Evaluate emerging LLMs, multimodal models, and local inference runtimes; prototype new tools and share findings with the team
• Contribute to Beinex thought leadership through internal knowledge-sharing sessions, technical write-ups, or industry presentations

Skills

Python

Hugging Face Transformer Library

Lang Chain

Lang Graph

FastAPI

PyTorch

LLM fine-tuning

prompt engineering

RAG pipeline design

agentic AI development

REST APIs

event-driven architectures

communication with stakeholders

Tools

Pinecone

Weaviate

Qdrant

FAISS

PG Vector

Docker

Kubernetes

AWS

Azure

GCP

Job description

Responsibilities

• Architect and deliver production-ready LLM applications including conversational agents, intelligent assistants, and RAG pipelines with advanced retrieval and re-ranking
• Build multi-agent systems using strong Lang Chain, Lang Graph, Auto Gen, or Crew AI
• Apply prompt engineering techniques to optimize model performance
• Integrate LLM solutions with enterprise systems via REST APIs and event-driven architectures
• Design agentic workflows with tool use, memory management, planning loops, and human-in-the-loop controls
• Build AI-driven automation pipelines across structured and unstructured enterprise data sources
• Design and manage vector stores Pinecone, Weaviate, Qdrant, FAISS, or PG Vector with semantic and hybrid search strategies
• Maintain knowledge bases powering enterprise AI applications, ensuring accuracy and freshness of indexed content
• Mentoring: guide and review the work of associate-level engineers; contribute to reusable frameworks and internal engineering standards
• Lead client workshops, technical discovery sessions, and proof-of-concept demonstrations; produce clear solution design documentation
• Evaluate emerging LLMs, multimodal models, and local inference runtimes; prototype new tools and share findings with the team
• Contribute to Beinex thought leadership through internal knowledge-sharing sessions, technical write-ups, or industry presentations

Qualifications & Experience

• 5+ years of professional experience in AI Engineering, Machine Learning, or Applied NLP, with at least 2 years focused on LLMs and Generative AI
• Strong proficiency in Python and experience with Hugging Face Transformer Library, Lang Chain, Lang Graph, FastAPI, and PyTorch
• Deep practical knowledge of LLM fine-tuning, prompt engineering, RAG pipeline design, and agentic AI development
• Hands-on experience with vector databases such as Pinecone, Weaviate, Qdrant, FAISS, or PG Vector, along with embedding models
• Proven ability to deploy and productionize AI solutions in AWS, Azure, or GCP using Docker and Kubernetes
• Experience integrating AI solutions with enterprise platforms through REST APIs, webhooks, or event-driven architectures
• Strong understanding of responsible AI principles, including hallucination mitigation, output evaluation, content safety, and model governance
• Excellent communication skills with the ability to explain complex technical concepts to both technical and business stakeholders
• Demonstrated ability to independently lead AI projects from solution design through production deployment
• Preferred Certifications: AWS Certified Machine Learning – Specialty, Azure AI Engineer, GCP Professional Machine Learning Engineer, or equivalent cloud AI/ML certification; Deep Learning Specialization; LLM Engineering or Generative AI certifications from recognized platforms; Certified Kubernetes Application Developer (CKAD) is an added advantage